The process of learning concepts is inherently dynamic, with both short-term neural changes as the aspects of the concept are progressively understood, and longer-term consolidation of the material for retrieval at future times. Higher forms of learning, such as verbal and mathematical reasoning, can be studied only in humans because they are beyond the known abilities of other species. Thus, non-invasive brain research technologies that provide both high-temporal and high-spatial resolution are required. The proposal represents an emerging domain in the neuroscience of learning, based on a recent development that enables the methodology of functional Magnetic Resonance Imaging (fMRI) to reveal the full range of the neural dynamics of conceptual learning in the human brain. Until now, fMRI has been a tool that could only study long-term neural changes because its response is based on the slow temporal dynamics of the cerebral blood response. As a result, it has been impossible to follow the temporal evolution of the learning process through the complex of local cortical activations in the human brain. An innovative approach will be taken to the analysis of neural dynamics to study the neural information flow in the brain during the learning of both verbal and mathematical reasoning tasks to determine how these two kinds of learning take place. In addition, a much higher spatial resolution of the fMRI technique than is usual in the study of cognitive processing will focus the study on the analysis of local neural populations. Our new technical developments allow us to resolve the neural dynamics of cognitive processing to their native temporal resolution and track the changes in dynamics as the conceptual learning proceeds. This novel approach is of high value in understanding the changes in neural population dynamics underlying the learning process in general, and of the processes of conceptual learning in particular.
The current proposal will provide knowledge of the neural dynamics of cognitive processing in the human brain that can have a transformative impact on our understanding of the learning process. In particular, it will radically enhance the ability to build computational models of the neural underpinnings of the learning process, and allow the sequence of information flow in the brain to be mapped as advanced concepts are learned for the first time. This novel capability will promote detailed strategies for enhancing information transfer about effective learning dynamics from the field of cognitive neuroscience to the educational arena.
